Domestic activities clustering from audio recordings using convolutional capsule autoencoder network

TL;DR

This paper introduces a convolutional capsule autoencoder network for clustering domestic activities from audio recordings, enabling unsupervised grouping of similar activity sounds with improved accuracy over existing methods.

Contribution

The study presents a novel CCAN model that learns deep embeddings for domestic activity clustering, outperforming state-of-the-art techniques on a public dataset.

Findings

Outperforms existing clustering methods in accuracy and mutual information

Effective unsupervised clustering of domestic activities from audio

Demonstrates potential for home activity monitoring applications

Abstract

Recent efforts have been made on domestic activities classification from audio recordings, especially the works submitted to the challenge of DCASE (Detection and Classification of Acoustic Scenes and Events) since 2018. In contrast, few studies were done on domestic activities clustering, which is a newly emerging problem. Domestic activities clustering from audio recordings aims at merging audio clips which belong to the same class of domestic activity into a single cluster. Domestic activities clustering is an effective way for unsupervised estimation of daily activities performed in home environment. In this study, we propose a method for domestic activities clustering using a convolutional capsule autoencoder network (CCAN). In the method, the deep embeddings are learned by the autoencoder in the CCAN, while the deep embeddings which belong to the same class of domestic activities are merged into a single cluster by a clustering layer in the CCAN. Evaluated on a public dataset adopted in DCASE-2018 Task 5, the results show that the proposed method outperforms state-of-the-art methods in terms of the metrics of clustering accuracy and normalized mutual information.